{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<i>Copyright (c) Microsoft Corporation. All rights reserved.</i>\n",
    "\n",
    "<i>Licensed under the MIT License.</i>\n",
    "\n",
    "\n",
    "# Testing of web services deployed on ACI and AKS\n",
    "\n",
    "## Table of content\n",
    "1. [Introduction](#intro)\n",
    "1. [Setup](#setup)\n",
    "  1. [Library import](#imports)\n",
    "  1. [Workspace retrieval](#workspace)\n",
    "  1. [Service retrieval](#service)\n",
    "1. [Testing of the web services](#testing)\n",
    "  1. [Using the *run* API](#run)\n",
    "  1. [Via a raw HTTP request](#request)\n",
    "1. [Service telemetry in Application Insights](#insights)\n",
    "1. [Clean up](#clean)\n",
    "  1. [Application Insights deactivation and web service termination](#del_app_insights)\n",
    "  1. [Docker image deletion](#del_image)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/ComputerVision/classification/notebooks/23_aci_aks_web_service_testing.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Introduction <a id=\"intro\"/>\n",
    "In the 2 prior notebooks, we deployed our machine learning model as a web service on [Azure Container Instances](https://github.com/Microsoft/ComputerVisionBestPractices/blob/master/classification/notebooks/21_deployment_on_azure_container_instances.ipynb) (ACI) and on [Azure Kubernetes Service](https://github.com/Microsoft/ComputerVision/blob/master/classification/notebooks/22_deployment_on_azure_kubernetes_service.ipynb) (AKS). In this notebook, we will learn how to test our service:\n",
    "- Using the `run` API\n",
    "- Via a raw HTTP request.\n",
    "\n",
    "<i><b>Note:</b> We are assuming that notebooks \"20\", \"21\" and \"22\" were previously run, and that we consequently already have a workspace, and a web service that serves our machine learning model on ACI and/or AKS. If that is not the case, we can refer to these three notebooks.</i>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Setup <a id=\"setup\"/>\n",
    "\n",
    "Let's start by retrieving all the elements we need; i.e. python libraries, Azure workspace and web services.\n",
    "\n",
    "### 2.A Library import <a id=\"imports\"/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Azure ML SDK Version: 1.0.48\n"
     ]
    }
   ],
   "source": [
    "# For automatic reloading of modified libraries\n",
    "%reload_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "# Regular python libraries\n",
    "import inspect\n",
    "import json\n",
    "import os\n",
    "import requests\n",
    "import sys\n",
    "from azureml.core.authentication import AzureCliAuthentication\n",
    "from azureml.core.authentication import InteractiveLoginAuthentication\n",
    "from azureml.core.authentication import AuthenticationException\n",
    "\n",
    "# fast.ai\n",
    "from fastai.vision import open_image\n",
    "\n",
    "# Azure\n",
    "import azureml.core\n",
    "from azureml.core import Workspace\n",
    "\n",
    "# Computer Vision repository\n",
    "sys.path.extend([\".\", \"../..\"])\n",
    "from utils_cv.common.data import data_path\n",
    "from utils_cv.common.image import im2base64, ims2strlist\n",
    "from utils_cv.common.azureml import get_or_create_workspace\n",
    "\n",
    "# Check core SDK version number\n",
    "print(f\"Azure ML SDK Version: {azureml.core.VERSION}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To create or access an Azure ML Workspace, you will need the following information. If you are coming from previous notebook you can retreive existing workspace, or create a new one if you are just starting with this notebook.\n",
    "\n",
    "- subscription ID: the ID of the Azure subscription we are using\n",
    "- resource group: the name of the resource group in which our workspace resides\n",
    "- workspace region: the geographical area in which our workspace resides (e.g. \"eastus2\" -- other examples are ---available here -- note the lack of spaces)\n",
    "- workspace name: the name of the workspace we want to create or retrieve."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": [
     "parameters"
    ]
   },
   "outputs": [],
   "source": [
    "\n",
    "subscription_id = \"YOUR_SUBSCRIPTION_ID\"\n",
    "resource_group = \"YOUR_RESOURCE_GROUP_NAME\"  \n",
    "workspace_name = \"YOUR_WORKSPACE_NAME\"  \n",
    "workspace_region = \"YOUR_WORKSPACE_REGION\" #Possible values eastus, eastus2 and so on.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.B Workspace retrieval <a id=\"workspace\">\n",
    "\n",
    "In [prior notebook](20_azure_workspace_setup.ipynb) notebook, we created a workspace. This is a critical object from which we will build all the pieces we need to deploy our model as a web service. Let's start by retrieving it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING - Warning: Falling back to use azure cli login credentials.\n",
      "If you run your code in unattended mode, i.e., where you can't give a user input, then we recommend to use ServicePrincipalAuthentication or MsiAuthentication.\n",
      "Please refer to aka.ms/aml-notebook-auth for different authentication mechanisms in azureml-sdk.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Workspace name: amlnotebookws\n",
      "Workspace region: eastus\n",
      "Resource group: amlnotebookrg\n"
     ]
    }
   ],
   "source": [
    "# A util method that creates a workspace or retrieves one if it exists, also takes care of Azure Authentication\n",
    "from utils_cv.common.azureml import get_or_create_workspace\n",
    "\n",
    "ws = get_or_create_workspace(\n",
    "        subscription_id,\n",
    "        resource_group,\n",
    "        workspace_name,\n",
    "        workspace_region)\n",
    "\n",
    "# Print the workspace attributes\n",
    "print('Workspace name: ' + ws.name, \n",
    "      'Workspace region: ' + ws.location, \n",
    "      'Subscription id: ' + ws.subscription_id, \n",
    "      'Resource group: ' + ws.resource_group, sep = '\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.C Service retrieval <a id=\"service\">\n",
    "\n",
    "If we have not deleted them in the 2 prior deployment notebooks, the web services we deployed on ACI and AKS should still be up and running. Let's check if that is indeed the case."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'aks-cpu-image-classif-web-svc': AksWebservice(workspace=Workspace.create(name='amlnotebookws', subscription_id='2ad17db4-e26d-4c9e-999e-adae9182530c', resource_group='amlnotebookrg'), name=aks-cpu-image-classif-web-svc, image_id=image-classif-resnet18-f48:2, compute_type=AKS, state=None, scoring_uri=http://13.82.180.139:80/api/v1/service/aks-cpu-image-classif-web-svc/score, tags={}, properties={'azureml.git.repository_uri': 'git@github.com:microsoft/ComputerVision.git', 'mlflow.source.git.repoURL': 'git@github.com:microsoft/ComputerVision.git', 'azureml.git.branch': 'rijai/amltesting', 'mlflow.source.git.branch': 'rijai/amltesting', 'azureml.git.commit': '0bf9dd30d64b5aed17a3e97055215e4d24b3840a', 'mlflow.source.git.commit': '0bf9dd30d64b5aed17a3e97055215e4d24b3840a', 'azureml.git.dirty': 'True'}),\n",
       " 'im-classif-websvc': AciWebservice(workspace=Workspace.create(name='amlnotebookws', subscription_id='2ad17db4-e26d-4c9e-999e-adae9182530c', resource_group='amlnotebookrg'), name=im-classif-websvc, image_id=image-classif-resnet18-f48:2, compute_type=ACI, state=None, scoring_uri=http://c7bf18e3-cef1-4179-a524-59f862ffa1d9.eastus.azurecontainer.io/score, tags={'webservice': 'image classification model (fastai 1.0.48)'}, properties={'azureml.git.repository_uri': 'git@github.com:microsoft/ComputerVision.git', 'mlflow.source.git.repoURL': 'git@github.com:microsoft/ComputerVision.git', 'azureml.git.branch': 'rijai/amltesting', 'mlflow.source.git.branch': 'rijai/amltesting', 'azureml.git.commit': '0bf9dd30d64b5aed17a3e97055215e4d24b3840a', 'mlflow.source.git.commit': '0bf9dd30d64b5aed17a3e97055215e4d24b3840a', 'azureml.git.dirty': 'True'})}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ws.webservices"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This command should return a dictionary, where the keys are the names we assigned to them.\n",
    "\n",
    "Let's now retrieve the web services of interest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Retrieve the web services\n",
    "aci_service = ws.webservices['im-classif-websvc']\n",
    "aks_service = ws.webservices['aks-cpu-image-classif-web-svc']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Testing of the web services <a id=\"testing\">\n",
    "\n",
    "Let's now test our web service. For this, we first need to retrieve test images and to pre-process them into the format expected by our service. A service typically expects input data to be in a JSON serializable format. Here, we use our own `ims2strlist()` function to transform our .jpg images into strings of bytes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "def im2base64(im_path: Union[Path, str]) -> bytes:\n",
      "    \"\"\"Get image bytes.\n",
      "\n",
      "    Args:\n",
      "        im_path (string): Path to the image\n",
      "\n",
      "    Returns:\n",
      "        im_bytes\n",
      "    \"\"\"\n",
      "\n",
      "    with open(im_path, \"rb\") as image:\n",
      "        # Extract image bytes\n",
      "        im_content = image.read()\n",
      "        # Convert bytes into a string\n",
      "        im_bytes = b64encode(im_content)\n",
      "\n",
      "    return im_bytes\n",
      "\n",
      "def ims2strlist(im_path_list: list) -> list:\n",
      "    \"\"\"Get byte-str list of the images in the given path.\n",
      "\n",
      "    Args:\n",
      "        im_path_list (list of strings): List of image paths\n",
      "\n",
      "    Returns:\n",
      "        im_string_list: List containing based64-encoded images\n",
      "            decoded into strings\n",
      "    \"\"\"\n",
      "\n",
      "    im_string_list = []\n",
      "    for im_path in im_path_list:\n",
      "        im_string_list.append(im2base64(im_path).decode(\"utf-8\"))\n",
      "\n",
      "    return im_string_list\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Check the source code of the conversion functions\n",
    "im2base64_source = inspect.getsource(im2base64)\n",
    "im2strlist_source = inspect.getsource(ims2strlist)\n",
    "print(im2base64_source)\n",
    "print(im2strlist_source)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract test images paths\n",
    "im_url_root = \"https://cvbp-secondary.z19.web.core.windows.net/images/\"\n",
    "im_filenames = [\"cvbp_milk_bottle.jpg\", \"cvbp_water_bottle.jpg\"]\n",
    "\n",
    "for im_filename in im_filenames:\n",
    "    # Retrieve test images from our storage blob\n",
    "    r = requests.get(os.path.join(im_url_root, im_filename))\n",
    "\n",
    "    # Copy test images to local data/ folder\n",
    "    with open(os.path.join(data_path(), im_filename), 'wb') as f:\n",
    "        f.write(r.content)\n",
    "\n",
    "# Extract local path to test images\n",
    "local_im_paths = [os.path.join(data_path(), im_filename) for im_filename in im_filenames]\n",
    "\n",
    "# Convert images to json object\n",
    "im_string_list = ims2strlist(local_im_paths)\n",
    "service_input = json.dumps({\"data\": im_string_list})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.A Using the *run* API <a id=\"run\">\n",
    "    \n",
    "In a real case scenario, we would only have one of these 2 services running. In this section, we show how to test that the web service running on ACI is working as expected. The commands we will use here are exactly the same as those we would use for our service running on AKS. We would just need to replace the `aci_service` object by the `aks_service` one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select the web service to test\n",
    "service = aci_service\n",
    "# service = aks_service"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predict using the deployed model\n",
    "result = service.run(service_input)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the results\n",
    "actual_labels = ['milk_bottle', 'water_bottle']\n",
    "for k in range(len(result)):\n",
    "    title = f\"{actual_labels[k]}/{result[k]['label']} - {round(100.*float(result[k]['probability']), 2)}%\"\n",
    "    open_image(local_im_paths[k]).show(title=title)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.B Via a raw HTTP request <a id=\"request\">\n",
    "\n",
    "In the case of AKS, we need to provide an authentication key. So let's look at the 2 examples separately, with the same testing data as before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "POST requests to url: http://c7bf18e3-cef1-4179-a524-59f862ffa1d9.eastus.azurecontainer.io/score\n",
      "Prediction: [{\"label\": \"water_bottle\", \"probability\": \"0.8001841306686401\"}, {\"label\": \"water_bottle\", \"probability\": \"0.68577641248703\"}]\n"
     ]
    }
   ],
   "source": [
    "# ---------\n",
    "# On ACI\n",
    "# ---------\n",
    "\n",
    "# Extract service URL\n",
    "service_uri = aci_service.scoring_uri\n",
    "print(f\"POST requests to url: {service_uri}\")\n",
    "\n",
    "# Prepare the data\n",
    "payload = {\"data\": im_string_list}\n",
    "\n",
    "# Send the service request\n",
    "resp = requests.post(service_uri, json=payload)\n",
    "\n",
    "# Alternative way of sending the test data\n",
    "# headers = {'Content-Type':'application/json'}\n",
    "# resp = requests.post(service_uri, service_input, headers=headers)\n",
    "\n",
    "print(f\"Prediction: {resp.text}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "POST requests to url: http://13.82.180.139:80/api/v1/service/aks-cpu-image-classif-web-svc/score\n",
      "Keys to use when calling the service from an external app: ['YeUcrNbnzUmN3kKrJipnGzNexQbp7nIz', 'ChjsMumEcKMdsM6R9Ov4XidvBKIJsEkb']\n",
      "Predictions: [{\"label\": \"water_bottle\", \"probability\": \"0.8001841306686401\"}, {\"label\": \"water_bottle\", \"probability\": \"0.68577641248703\"}]\n"
     ]
    }
   ],
   "source": [
    "# ---------\n",
    "# On AKS\n",
    "# ---------\n",
    "\n",
    "# Service URL\n",
    "service_uri = aks_service.scoring_uri\n",
    "print(f\"POST requests to url: {service_uri}\")\n",
    "\n",
    "# Prepare the data\n",
    "payload = {\"data\": im_string_list}\n",
    "\n",
    "# - - - - Specific to AKS - - - -\n",
    "# Authentication keys\n",
    "primary, secondary = aks_service.get_keys()\n",
    "print(f\"Keys to use when calling the service from an external app: {[primary, secondary]}\")\n",
    "\n",
    "# Build the request's parameters\n",
    "key = primary\n",
    "# Set the content type\n",
    "headers = { 'Content-Type':'application/json' }\n",
    "# Set the authorization header\n",
    "headers['Authorization']=f'Bearer {key}'\n",
    "# - - - - - - - - - - - - - - - -\n",
    "\n",
    "# Send the service request\n",
    "resp = requests.post(service_uri, json=payload, headers=headers)\n",
    "# Alternative way of sending the test data\n",
    "# resp = requests.post(service_uri, service_input, headers=headers)\n",
    "\n",
    "print(f\"Predictions: {resp.text}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Service telemetry in [Application Insights](https://docs.microsoft.com/en-us/azure/azure-monitor/app/app-insights-overview) <a id=\"insights\"/>\n",
    "\n",
    "Let's now assume that we have users, and that they start sending requests to our web service. As they do so, we want to ensure that our service is up, healthy, returning responses in a timely fashion, and that traffic is reasonable for the resources we allocated to it. For this, we can use Application Insights. This service captures our web service's logs, parses them and provides us with tables and visual representations of what is happening.\n",
    "\n",
    "In the [Azure portal](https://portal.azure.com):\n",
    "- Let's navigate to \"Resource groups\"\n",
    "- Select our subscription and resource group that contain our workspace\n",
    "- Select the Application Insights type associated with our workspace\n",
    "  * _If we have several, we can still go back to our workspace (in the portal) and click on \"Overview\" - This shows the elements associated with our workspace, in particular our Application Insights, on the upper right of the screen_\n",
    "- Click on the App Insights resource\n",
    "    - There, we can see a high level dashboard with information on successful and failed requests, server response time and availability (cf. Figure 1)\n",
    "- Click on the \"Server requests\" graph\n",
    "- In the \"View in Analytics\" drop-down, select \"Request count\" in the \"Analytics\" section\n",
    "    - This displays the specific query ran against the service logs to extract the number of executed requests (successful or not -- cf. Figure 2).\n",
    "- Still in the \"Logs\" page, click on the eye icon next to \"requests\" on the \"Schema\"/left pane, and on \"Table\", on the right:\n",
    "    - This shows the list of calls to the service, with their success statuses, durations, and other metrics. This table is especially useful to investigate problematic requests (cf. Figure 3).\n",
    "    - Results can also be visualized as a graph by clicking on the \"Chart\" tab. Metrics are plotted by default, but we can change them by clicking on one of the field name drop-downs (cf. Figures 4 to 6).\n",
    "- Navigate across the different queries we ran through the different \"New Query X\" tabs.\n",
    "\n",
    "<table>\n",
    "<tr>\n",
    "    <td>\n",
    "        <img src=\"media/webservice_performance_metrics.jpg\"  width=\"400\"/>Figure 1: Web service performance metrics\n",
    "    </td>\n",
    "    <td>\n",
    "        <img src=\"media/application_insights_all_charts.jpg\" width=\"500\"/>Figure 2: Insights into failed requests\n",
    "    </td>\n",
    "</tr>\n",
    "\n",
    "<tr>\n",
    "    <td>\n",
    "        <img src=\"media/logs_failed_request_details.jpg\" width=\"500\"/>Figure 3: Example log of a failed request\n",
    "    </td>\n",
    "    <td>\n",
    "        <img src=\"media/all_requests_line_chart.jpg\"  width=\"400\" alt=\"my alt text\"/>Figure 4: Total request count over time\n",
    "    </td>\n",
    "</tr>\n",
    "\n",
    "\n",
    "<tr>\n",
    "    <td>\n",
    "        <img src=\"media/failures_requests_line_chart.jpg\" width=\"500\"/>Figure 5: Failed request count over time\n",
    "    </td>\n",
    "    <td>\n",
    "        <img src=\"media/success_status_bar_chart.jpg\"  width=\"400\" alt=\"my alt text\"/>Figure 6: Request success distribution\n",
    "    </td>\n",
    "</tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Clean up <a id=\"clean\">\n",
    "\n",
    "In a real-life scenario, it is likely that one of our web services would need to be up and running at all times. However, in the present demonstrative case, and now that we have verified that they work, we can delete them as well as all the resources we used.\n",
    "\n",
    "Overall, with a workspace, a web service running on ACI and another one running on a CPU-based AKS cluster, we incurred a cost of about $15 a day (as of May 2019). About 70% was spent on virtual machines, 13% on the container registry (ACR), 12% on the container instances (ACI), and 5% on storage.\n",
    "\n",
    "To get a better sense of pricing, we can refer to [this calculator](https://azure.microsoft.com/en-us/pricing/calculator/?service=virtual-machines). We can also navigate to the [Cost Management + Billing pane](https://ms.portal.azure.com/#blade/Microsoft_Azure_Billing/ModernBillingMenuBlade/BillingAccounts) on the portal, click on our subscription ID, and click on the Cost Analysis tab to check our credit usage.\n",
    "\n",
    "<i><b>Note:</b> In the next notebooks, we will continue to use the AKS web service. This is why we are only deleting the service deployed on ACI.</i>\n",
    "\n",
    "### 5.A Application Insights deactivation and web service termination <a id=\"del_app_insights\">\n",
    "\n",
    "When deleting resources, we need to start by the ones we created last. So, we first deactivate the telemetry and then delete services and compute targets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Telemetry deactivation\n",
    "# aks_service.update(enable_app_insights=False)\n",
    "\n",
    "# Services termination\n",
    "aci_service.delete()\n",
    "# aks_service.delete()\n",
    "\n",
    "# Compute target deletion\n",
    "# aks_target.delete()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.B Docker image deletion <a id=\"del_image\">\n",
    "\n",
    "Now that the services no longer exist, we can delete the Docker image that we created in [21_deployment_on_azure_container_instances.ipynb](https://github.com/Microsoft/ComputerVisionBestPractices/blob/master/classification/notebooks/21_deployment_on_azure_container_instances.ipynb), and which contains our image classifier model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Docker images:\n",
      "    --> Name: image-classif-resnet18-f48\n",
      "    --> ID: image-classif-resnet18-f48:2\n",
      "    --> Tags: {'training set': 'ImageNet', 'architecture': 'CNN ResNet18', 'type': 'Pretrained'}\n",
      "    --> Creation time: 2019-07-18 17:51:26.927240+00:00\n"
     ]
    }
   ],
   "source": [
    "print(\"Docker images:\")\n",
    "for docker_im in ws.images:    \n",
    "    print(f\"    --> Name: {ws.images[docker_im].name}\\n\\\n",
    "    --> ID: {ws.images[docker_im].id}\\n\\\n",
    "    --> Tags: {ws.images[docker_im].tags}\\n\\\n",
    "    --> Creation time: {ws.images[docker_im].created_time}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "docker_image = ws.images[\"image-classif-resnet18-f48\"]\n",
    "# docker_image.delete()"
   ]
  }
 ],
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